<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>32(1)</volume><submitter>Kossmeier M</submitter><pubmed_abstract>Citations are widely used for measuring scientific impact. The goal of the present study was to predict citation counts of manuscripts submitted to Transplant International (TI) in the two calendar years following publication. We considered a comprehensive set of 21 manuscript, author, and peer-review-related predictor variables available early in the peer-review process. We also evaluated how successfully the peer-review process at TI identified and accepted the most promising manuscripts for publication. A developed predictive model with nine selected variables showed acceptable test performance to identify often cited articles (AUROC = 0.685). Particularly important predictors were the number of pages, month of publication, publication type (review versus other), and study on humans (ye</pubmed_abstract><journal>Transplant international : official journal of the European Society for Organ Transplantation</journal><pagination>6-15</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7379680</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Predicting future citation counts of scientific manuscripts submitted for publication: a cohort study in transplantology.</pubmed_title><pmcid>PMC7379680</pmcid><pubmed_authors>Kossmeier M</pubmed_authors><pubmed_authors>Heinze G</pubmed_authors></additional><is_claimable>false</is_claimable><name>Predicting future citation counts of scientific manuscripts submitted for publication: a cohort study in transplantology.</name><description>Citations are widely used for measuring scientific impact. The goal of the present study was to predict citation counts of manuscripts submitted to Transplant International (TI) in the two calendar years following publication. We considered a comprehensive set of 21 manuscript, author, and peer-review-related predictor variables available early in the peer-review process. We also evaluated how successfully the peer-review process at TI identified and accepted the most promising manuscripts for publication. A developed predictive model with nine selected variables showed acceptable test performance to identify often cited articles (AUROC = 0.685). Particularly important predictors were the number of pages, month of publication, publication type (review versus other), and study on humans (ye</description><dates><release>2019-01-01T00:00:00Z</release><publication>2019 Jan</publication><modification>2025-04-05T13:44:28.612Z</modification><creation>2025-04-05T13:44:28.612Z</creation></dates><accession>S-EPMC7379680</accession><cross_references><pubmed>29907979</pubmed><doi>10.1111/tri.13292</doi></cross_references></HashMap>